A methodology to build multi-compartment lumped elements equivalent circuits for the neuron/electrode systems is proposed. The equivalent circuit topology is derived by careful scrutiny of accurate multiphysics finite-elements method (FEM) simulations that couple ion transport in the intra- and extracellular fluids, activation of ion channels in the cellular membrane, and signal collection by the electronic readout, thus improving upon most common area contact models. We show that the equivalent circuits derived with our method match with good accuracy the reference FEM simulations over a wide range of geometrical/physical parameters such as the neuron and electrode size, the thickness of the electrolytic cleft, the input impedance of the readout amplifier, even in presence of nonuniform ion channel distributions. The impact of the number of compartments on the model accuracy is also analyzed in detail. We finally illustrate by FEM simulations the effect of extracellular ion transport on the reversal potentials of the Hodgkin-Huxley neuron model and how it can affect the recorded signal for very thin electrolyte clefts between the neuron and the electrode, in a way not yet captured by equivalent circuits of the neuron/electrode system.

A three-dimensional finite element method (FEM) simulation of a neuron (retinal ganglion cell) interfaced to a high-density CMOS-based microelectrode array (MEA) is performed and compared to recordings. The adopted multiscale simulation approach accurately reproduces the signals recorded by the patch clamp and the MEA sensors. The simulation leads us to identify bendings of the axon initial segment of a few neurons embedded in tissue, which are reported here for the first time.

In the context of in-vitro neural interfaces for neurostimulation via extracellular calcium modulation, we in-vestigated by finite element numerical simulations the electrical cross-talk between a polymeric ionic actuator for calcium release and a microelectrode for neural recording. Several device designs have been explored to mitigate the cross-talk. A separation wall between the ionic emitter and the sensing electrode has been found to remarkably improve the ratio between the sensed action potentials and the disturb induced by ionic actuation.

Understanding the biological-electrical transduction mechanisms is essential for reliable neural signal recording and feature extraction. As an alternative to state-of-the-art lumped-element circuit models, here we adopt a multiscale-multiphysics finite-element modeling framework. The model couples ion transport with the Hodgkin-Huxley model and the readout circuit, and is used to investigate a few relevant case studies. This approach is amenable to explore ion transport in the extracellular medium otherwise invisible to circuit model analysis.

Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for in vitro studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio.This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Active FET-based nanoelectrodes are promising candidates to serve as sensors for neural signal recording. Based on a multiscale-multiphysics TCAD modelling framework, we study the interaction of two representative nanoelectrode archi-tectures in intracellular contact with neurons. The methodology is explained, and DC, AC, and transient simulations are exten-sively used to compare the main performance metrics of the proposed structures. The lateral coating of the nanoelectrode results to be a key-parameter to control the sensor performance.

A design-oriented numerical study of vertical Si-nanowires to be used as sensing elements for the detection of the intracellular electrical activity of neurons. An equivalent lumped-element circuit model is derived and validated by comparison with physics-based numerical simulations. Most of the component values can be identified individually by geometrical and physical considerations. The transfer function and the SNR of the sensor in presence of thermal noise are derived, and the impact of the device geometry is shown.